Predicting S. aureus antimicrobial resistance with interpretable genomic space maps
Karina Pikalyova,
Alexey Orlov,
Dragos Horvath
et al.
Abstract:Increasing antimicrobial resistance (AMR) represents a global healthcare threat. To decrease the spread of AMR and associated mortality, methods for rapid selection of optimal antibiotic treatment are urgently needed. Machine learning (ML) models based on genomic data to predict resistant phenotypes can serve as a fast screening tool prior to phenotypic testing. Nonetheless, many existing ML methods lack interpretability. Therefore, we present a methodology for visualization of sequence space and AMR predictio… Show more
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